Technical Note: A dose calculation framework for dynamic electron arc radiotherapy (DEAR) using VirtuaLinac Monte Carlo simulation tool.

Journal Article

PURPOSE: Dynamic electron arc radiotherapy (DEAR) is a novel dynamic technique that achieves highly conformal dose through simultaneous couch and gantry motion during delivery. The purpose of this study is to develop a framework integrating a Monte Carlo dose engine (VirtuaLinac) to a treatment planning system (TPS, Eclipse) for DEAR. A quality assurance (QA) procedure is also developed. METHODS AND MATERIALS: The interfaces include the following: computed tomography image export and conversion for VirtuaLinac; VirtuaLinac computation tasks management through application programming interface (API); and dose matrix processing and evaluation. The framework was validated with both static beam and DEAR plan with a 3 × 3 cm2 cutout for both 6 and 9 MeV electrons. Verification plans for DEAR were created on flat phantom and a hybrid dose calculation technique was developed which convolves precalculated small field kernel with the beam trajectory, and the resulting dose was compared with the full VirtuaLinac calculation and film measurement. RESULTS: Excellent agreement between VirtuaLinac and eMC was observed with three-dimensional γ pass rate of 98% at 1%/1 mm criteria for both 6 and 9 MeV electrons. Film measurement shows two-dimensional (2D) γ passing rate of 99.8 % (6 MeV) and 97.1% (9 MeV) at 2%/2 mm criteria. For DEAR plans the comparison of VirtuaLinac and measurement shows the 2D γ passing rates of 94% at 2%/2 mm for 6 MeV. The dose distributions from hybrid method in phantom are identical to the full VirtuaLinac simulations, but can be done instantly. CONCLUSIONS: A framework has been developed for DEAR dose calculation using VirtuaLinac Monte Carlo dose engine. The VirtuaLinac calculated dose was validated against measurement. A feasible and practical DEAR QA method has been developed for dose measurement in phantom. The hybrid dose calculation technique is efficient and suitable for DEAR QA purpose.

Full Text

Duke Authors

Cited Authors

  • Wang, X; Sawkey, D; Wu, Q

Published Date

  • January 2020

Published In

Volume / Issue

  • 47 / 1

Start / End Page

  • 164 - 170

PubMed ID

  • 31667858

Pubmed Central ID

  • 31667858

Electronic International Standard Serial Number (EISSN)

  • 2473-4209

Digital Object Identifier (DOI)

  • 10.1002/mp.13882

Language

  • eng

Conference Location

  • United States